{"id":"W1828791356","doi":"10.1109/iscas.2004.1328936","title":"Craniofacial landmarks extraction by Partial Least Squares Regression","year":2004,"lang":"en","type":"article","venue":"","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Partial least squares regression; Artificial intelligence; Rotation (mathematics); Pattern recognition (psychology); Set (abstract data type); Craniofacial; Regression; Computer vision; Computer science; Mathematics; Feature extraction; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005433863,0.001119814,0.0009765737,0.001119285,0.0002808922,0.000547093,0.0008729342,0.0005428127,0.001825474],"category_scores_gemma":[0.002104354,0.0006729349,0.0008441294,0.00115118,0.0003445642,0.0007353026,0.0005582077,0.0008931698,0.002415757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001972211,"about_ca_system_score_gemma":0.0006146088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002226045,"about_ca_topic_score_gemma":0.002647272,"domain_scores_codex":[0.9993737,0.0001150779,0.0000263246,0.0001893396,0.0002584168,0.00003715562],"domain_scores_gemma":[0.9994748,0.0001729165,0.00009277109,0.00007994097,0.0001645256,0.00001496022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001646969,0.00004018901,0.00179916,0.000174838,0.0001099861,0.0001778018,0.00006455425,0.06170393,0.09689552,0.0020119,0.002892085,0.8339654],"study_design_scores_gemma":[0.00002662814,0.0001444013,0.005122845,0.00002531616,0.00007305493,0.0007558583,0.00006436581,0.9067537,0.07640756,0.003102134,0.007465761,0.00005831004],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006422704,0.0001434126,0.9916158,0.00003478801,0.00001303553,0.00001760951,0.00007218087,0.001417054,0.0002633278],"genre_scores_gemma":[0.1541677,0.0005175919,0.8408304,0.00005555365,0.0000513003,0.00009349221,0.0008617959,0.0004700759,0.002951902],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002226045,"threshold_uncertainty_score":0.006106853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009710299075780044,"score_gpt":0.2818068813343189,"score_spread":0.2720965822585389,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}